Top 10 Events

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import warnings
warnings.filterwarnings("ignore")

import os
import os.path as op
import sys
import folium

import numpy as np
import pandas as pd

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.colors import get_df_col
from ind_setup.tables import plot_df_table, get_data_metrics, table_temperature_summary

sys.path.append("../../../functions")
from data_downloaders import GHCN

Define location and variables of interest

country = 'Palau'
vars_interest = ['TMIN', 'TMAX']

Get Data

https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures" 

Using Koror Station

Analysis of how much the maximum and minimum temperatures over time are changing.
The analysis of the difference between these 2 variables will allow us to know how the daily variability is being modified

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if update_data:
    df_country = GHCN.get_country_code(country)
    print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')

    df_stations = GHCN.download_stations_info()
    df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
    print(f'There are {df_country_stations.shape[0]} stations in {country}')

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if update_data:
    GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
    id = 'PSW00040309' # Koror Station
    dict_min = GHCN.extract_dict_data_var(GHCND_dir, 'TMIN', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
    dict_max = GHCN.extract_dict_data_var(GHCND_dir, 'TMAX', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
    st_data = pd.concat([dict_min['data'], (dict_max['data'])], axis=1).dropna()
    st_data['diff'] = st_data['TMAX'] - st_data['TMIN']
    st_data['TMEAN'] = (st_data['TMAX'] + st_data['TMIN'])/2
    st_data.to_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))
else:
    st_data = pd.read_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))

df = table_temperature_summary(st_data)
fig = plot_df_table(df.T, figsize = (500, 340),)

Monthly

st_data = st_data.resample('M').mean()

nevents = 10
top_10 = st_data.sort_values(by='TMAX', ascending=False).head(nevents)

dict_plot = [{'data' : st_data, 'var' : 'TMAX', 'ax' : 1, 'label' : 'TMAX'}]
scatter_dict = [{'data' : top_10, 'var' : 'TMAX', 'ax' : 1, 'label' : 'Top 10 Events'}]

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = scatter_dict, figsize = (25, 12))

fig = plot_df_table(np.round(top_10, 2)[['TMAX']])

Annual

st_data = st_data.resample('Y').mean()
top_10 = st_data.sort_values(by='TMAX', ascending=False).head(10)

dict_plot = [{'data' : st_data, 'var' : 'TMAX', 'ax' : 1, 'label' : 'TMAX'}]
scatter_dict = [{'data' : top_10, 'var' : 'TMAX', 'ax' : 1, 'label' : 'Top 10 Events'}]
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = scatter_dict, figsize = (25, 12));

fig = plot_df_table(np.round(top_10, 2)[['TMAX']])

st_data

	TMIN	TMAX	diff	TMEAN
DATE
1951-12-31	24.150806	30.923638	6.772832	27.537222
1952-12-31	23.838715	30.904048	7.065333	27.371381
1953-12-31	24.084018	31.039644	6.955627	27.561831
1954-12-31	24.978473	31.087915	6.109442	28.033194
1955-12-31	23.757035	30.740156	6.983121	27.248596
...	...	...	...	...
2021-12-31	24.750006	28.620488	3.870482	26.685247
2022-12-31	24.567689	29.918439	5.350750	27.243064
2023-12-31	25.550660	30.044822	4.494161	27.797741
2024-12-31	25.849527	30.151326	4.301799	28.000426
2025-12-31	25.641176	30.170588	4.529412	27.905882

75 rows × 4 columns